Viral load suppression among patients receiving antiretroviral therapy in outpatient clinics in Democratic Republic of Congo
Bibliographic record
Abstract
Introduction: Education and disease management have resulted in global decline of new HIV infections, from 2.8 million a year in 2000 to about 1.5 million in 2020 (46% reduction) as well as number of deaths, from 1.4 million in 2000 to 690,000 a year in 2020 (51% reduction).The purpose of this study was to examine factors associated with viral load (VL) suppression (< 1,000 copies/ml), including age, sex, and geographic and clinical characteristics of patients on antiretroviral therapy (ART) in outpatient clinics in Kinshasa and Haut-Katanga Provinces, Democratic Republic of Congo (DRC). Material and methods:Using a retrospective cohort study design, we analyzed data of 5,338 people living with HIV (PLHIV) on ART from 116 HIV/AIDS clinics located in the Haut-Katanga and Kinshasa Provinces in DRC.c 2 and multivariable logistic regression analyses were applied.Results: Age and urban health zones were significantly associated with VL suppression.Eighty-six percent of adult patients (15 years or older) had achieved a VL suppression, compared to 73.5% of patients younger than 15 years.Average time on ART was less than three years, and majority of participants were 15 years of age or older, females, and mostly living in urban areas.Conclusions: Our findings indicated that younger patients on ART and patients living in semi-rural areas (vs.urban) had a significantly lower probability of risk of VL suppression, underscoring the need for enhanced efforts targeting these populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".